title: Automated detection of malaria in Giemsa-stained thin blood smears.
abstract: The current gold standard of malaria diagnosis is the manual, microscopy-based analysis of Giemsa-stained blood smears, which is a time-consuming process requiring skilled technicians. This paper presents an algorithm that identifies and counts red blood cells (RBCs) as well as stained parasites in order to perform a parasitaemia calculation. Morphological operations and histogram-based thresholding are used to extract the red blood cells. Boundary curvature calculations and Delaunay triangulation are used to split clumped red blood cells. The stained parasites are classified using a Bayesian classifier with their RGB pixel values as features. The results show 98.5% sensitivity and 97.2% specificity for detecting infected red blood cells.
authors.forename: Mark C
authors.initials: MC
authors.lastname: Mushabe
authors.forename: Ronald
authors.initials: R
authors.lastname: Dendere
authors.forename: Tania S
authors.initials: TS
authors.lastname: Douglas
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D001399:Azure Stains / Q000378:metabolism*; D001499:Bayes Theorem; D004906:Erythrocyte Count; D004912:Erythrocytes / Q000469:parasitology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016778:Malaria, Falciparum / Q000097:blood* / Q000175:diagnosis*; D008853:Microscopy; D010963:Plasmodium falciparum / Q000378:metabolism / Q000502:physiology; D012680:Sensitivity and Specificity
chemical_list: D001399:Azure Stains